Vol. 7 No. 1 (2025), Articles
Vol. 7 No. 1 (2025)

Design and Implementation of Short-Term Load Forecasting using LightGBM

Articles
Published 21-04-2025
Surendar S.
Department of EEE, Coimbatore Institute of Technology, Anna University, Coimbatore, India
Midhun Keshava Abimanyu M.E.
Department of EEE, Coimbatore Institute of Technology, Anna University, Coimbatore, India
Sweetha M.
Department of EEE, Coimbatore Institute of Technology, Anna University, Coimbatore, India
Syamala M.
Department of EEE, Coimbatore Institute of Technology, Anna University, Coimbatore, India
Manikandan V.
Professor, Department of EEE, Coimbatore Institute of Technology, Anna University, Coimbatore, India
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Keywords

Real-Time Power Monitoring
Load Forecasting
Machine Learning
Energy Management
Cloud Database

How to Cite

S., Surendar, Midhun Keshava Abimanyu M.E., Sweetha M., Syamala M., and Manikandan V. 2025. “Design and Implementation of Short-Term Load Forecasting Using LightGBM”. Journal of Artificial Intelligence and Capsule Networks 7 (1): 51-64. https://doi.org/10.36548/jaicn.2025.1.004.
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Abstract

In the modern world, energy efficiency and smart systems are more necessary than ever before, making it imperative to possess a system capable of monitoring and forecasting power consumption in real-time. This research presents an advanced Data Acquisition System (DAS) combining hardware sensors, cloud computing, and machine learning to deliver accurate power monitoring and forecasting. The system employs sensors connected to an Arduino and ESP8266, which wirelessly transmit voltage and current information to Firebase for processing and storage. A machine learning algorithm implemented in Python subsequently forecasts power demand, with the outcomes displayed on a user-friendly web dashboard developed with Flask. This dashboard updates dynamically, displaying real-time power information and visualizing predictions every five minutes. Through the implementation of REST APIs, the system ensures smooth and efficient data transmission without requiring local storage. This research provides a real-world and cost-efficient solution that can integrate energy management systems with intelligent, real-time knowledge for better decision-making.

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References

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